Systems and methods for categorizing and presenting performance assessment data

ABSTRACT

The field of the invention relates to systems and methods for data mining and processing, and more particularly to systems and methods for automating content from performance assessment data. In one embodiment, an automated notes and categorization system may include a primary database comprising performance assessment data. The primary database is operatively coupled to a computer program product having a computer-usable medium having a sequence of instructions which, when executed by a processor, causes said processor to execute a process that analyzes and converts raw performance data into automated content that presents data in readable user friendly form to facilitate human understanding.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/164,584, filed Oct. 18, 2018, which is a continuation of U.S. patentapplication Ser. No. 15/897,111, filed Feb. 14, 2018 (now abandoned),which is a continuation of U.S. patent application Ser. No. 15/164,750,filed May 25, 2016 (now U.S. Pat. No. 9,934,298), which is acontinuation of U.S. patent application Ser. No. 14/548,211, filed Nov.19, 2014 (now U.S. Pat. No. 9,378,251), which is a continuation of U.S.patent application Ser. No. 14/069,256, filed Oct. 31, 2013 (nowabandoned), which is a continuation of U.S. patent application Ser. No.13/611,188, filed Sep. 12, 2012 (now abandoned), which claims thebenefit of and priority to U.S. Provisional Application No. 61/533,936,filed Sep. 13, 2011, the disclosures of all of which are incorporatedherein by reference in their entireties for all purposes.

FIELD OF THE INVENTION

The field of the invention relates to systems and methods for datamining and processing, and more particularly to systems and methods forautomating content from performance assessment data.

BACKGROUND OF THE INVENTION

Performance assessment data is an important aspect of the business,analysis, and appreciation of professional/fantasy sports, stockmarkets, mutual funds, personal fitness, student education, videogaming, consumer sales, and so on. Athletic teams, coaches, scouts,agents, and fans evaluate performance data and statistics for comparingthe performance of teams and individual athletes. Game strategy andplayer potential are often based on predictive models using this data.Similarly, organizations and individuals evaluate corporate performancedata to rank performance, reward good performance, and providedevelopment assistance. Student test scores are also used to evaluateeducational strategy; fitness and health statistics are monitored forefficient personal training; and financial charts are analyzed toalleviate stock risks. The advantages of data processing and analysisare well understood and appreciated. However, data processing andanalysis is not always user friendly as understanding a large amount ofstructured data is a daunting task.

One approach for making sense of raw performance data relies on humanexpertise. For example, in the area of athletics, domain experts (e.g.,coaches, scouts, managers, analysts, statisticians etc.) are typicallyrelied on to effectively convert raw data into human readable/usefulknowledge. Batting averages, field goal percentages, successive streaksare considered, inter alia, to determine success against certain playersor potential against future opponents. Human domain experts can“humanize” this raw data and convert numbers and statistics intoinsightful prose/narrative. But, effectively analyzing performance datarequires consideration of incredible amounts of information to reducevariable uncertainty. Bulk number crunching becomes a difficult taskwhen the valuable insight is drowned in a sea of numbers and statistics.Therefore, this manual based approach to identifying performance metricsconsumes both time and resources.

In another example, the popularity of fantasy sports has convertedmillions of fans into expert statisticians for scrutinizing aprofessional athlete's performance data. Fantasy leagues allow thevirtual assembly of teams comprising actual athletes to compete withother virtual teams based upon those players' real-life performance. Thesports and players represented through fantasy games are widespread. Thenumber of applications providing fantasy leagues, often over theInternet, is similarly extensive. However, each may provide a unique wayof scoring and rewarding player performance. Accordingly, the value ofeach player's performance data may vary across different leagues andsports.

Advancements in technology and computerized data processing have made awealth of performance statistics readily available for coaches andfantasy owners alike to review. Individual player statistics may giveinsight to an athlete's speed, movement, skills, and agility against oneor more opponents. However, processing this data and placing value onrelevant statistics varies between managers, leagues, and sports.Manually digesting performance data can be cumbersome in light of thecurrent number of statistical categories monitored. As the type andnumber of data collected increased, more practical methods weredeveloped for useful volumetric data processing.

In one approach for volumetric processing of raw performance statistics,predictive modeling systems are used. Using a more automated approach toanalyze a large quantity of data, an example modeling system associatedwith fantasy sports leagues is disclosed in U.S. patent application Ser.No. 12/111,054, U.S. Publication No. 2008/0281444 A1, filed Apr. 28,2008, to Krieger et al. for a “Predictive Modeling System and Method forFantasy Sports,” which is hereby incorporated by reference in itsentirety. This system contemplates a predictive modeling engine forgenerating relationships among player data and provides projectionsbased on the relationships.

However, current systems for predictive analytics typically generalizeknown patterns to new data for projecting player performances.Additionally, these predictive modeling systems rarely consider theunique priority various users place on certain data sets. The predictiveresults are typically as hard to digest and read as the raw data itselfto the average human user.

In contrast to generalizing known patterns to new data, data miningemphasizes discovering previously unknown patterns in new data sets.Data mining has recently experienced growth in the area of performanceassessment. Performance assessment benefits from discovering unknownstrengths and weaknesses as opposed to assessing patterns of currentperformance. The advantages of domain experts (e.g., coaches, teachers,interactive gamers, and the like) in analyzing performance metrics arebased on the inherent expertise of these individuals to detect unknownpatterns through subjective approaches. Therefore, an effective methodof automatically analyzing performance assessment data enhancesalternative statistical evaluation with data mining to discover patternsthat are systematically difficult to detect, especially when dealingwith dynamic data sets.

Additionally, current systems modeling performance assessment may notprovide results in a user-friendly manner, as discussed above.Supplemental tables and graphs are often created to reflect the resultsof predictive modeling and still require additional processing andanalysis. Subjective priority is neither accounted for nor presented andadditional steps of manual data processing required. Accordingly, animproved system and method for automated processing, categorizing, andpresenting performance assessment data is desirable.

SUMMARY OF THE INVENTION

The field of the invention relates to systems and methods for datamining and processing, and more particularly to systems and methods forautomating performance content from performance assessment data. In oneembodiment, an automated notes and categorization system may include aprimary database comprising performance assessment data. The primarydatabase is operatively coupled to a computer program product having acomputer-usable medium having a sequence of instructions which, whenexecuted by a processor, causes said processor to execute a process thatanalyzes and converts raw performance data. The system further includesa processed database for storing the processed data operatively coupledto the computer program product for use with various user applications.

The process includes the steps of data mining said performanceassessment data to obtain summarized data; prioritizing summarized databased on user-defined weight values for a plurality of classificationcategories; and converting results of the prioritization into plainlanguage notes. The automated plain language notes will facilitate humanunderstanding by presenting the data in narrative fashion.

Other systems, methods, features and advantages of the invention will beor will become apparent to one with skill in the art upon examination ofthe following figures and detailed description. It is intended that allsuch additional systems, methods, features and advantages be includedwithin this description, be within the scope of the invention, and beprotected by the accompanying claims.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to better appreciate how the above-recited and other advantagesand objects of the inventions are obtained, a more particulardescription of the embodiments briefly described above will be renderedby reference to specific embodiments thereof, which are illustrated inthe accompanying drawings. It should be noted that the components in thefigures are not necessarily to scale, emphasis instead being placed uponillustrating the principles of the invention. Moreover, in the figures,like reference numerals designate corresponding parts throughout thedifferent views. However, like parts do not always have like referencenumerals. Moreover, all illustrations are intended to convey concepts,where relative sizes, shapes and other detailed attributes may beillustrated schematically rather than literally or precisely.

FIG. 1 is a schematic diagram of a data processing system for use withperformance assessment data according to one embedment of the presentinvention.

FIG. 2 is a flowchart of an electronic process in accordance with apreferred embodiment of the present invention.

FIG. 3 is a flowchart further detailing the electronic process shown inFIG. 2 in accordance with a preferred embodiment of the presentinvention.

FIG. 4 is another flowchart further detailing the electronic processshown in FIG. 2 in accordance with a preferred embodiment of the presentinvention;

FIG. 5a is an example of a user interface for a football application ofthe performance assessment application;

FIG. 5b is another example of a user interface for a footballapplication of the performance assessment application;

FIG. 6 is another flowchart detailing another electronic process inaccordance with a preferred embodiment of the present invention;

FIGS. 7a-g are other examples of user interfaces of the performanceassessment application; and

FIG. 8 is another example of a user interface of the performanceassessment application.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

As described above, an incredible amount of performance assessment dataexists across a number of domains of athletics, interactive gaming,physical fitness, finance, and so on. Evaluating this performance datafor decision-making criteria is an integral part for seeking acompetitive edge in each of these domains. Turning to FIG. 1, anexemplary system 100 is illustrated to make sense of raw performancedata and convert said data into actionable knowledge, according to oneembodiment of the present invention. The system 100 has a processingapplication 101 that is communicatively coupled to primary data source102. As is known in the art, processing application 101 may represent acomputer, which includes a computer-usable storage medium, such as in aserver, having sequence of instructions which, when executed by aprocessor, causes said processor to execute a process that converts rawperformance data into information that is both digestible andprioritized.

The processing application 101 may further include a user interfaceconsole, such as a touch screen monitor (not shown), to allow theuser/operator to preset various system parameters. User defined systemparameters may include, but are not limited to, assigning categoricalpriorities, assigning priority weights, adjusting time-frame analysis,and setting split variables.

In one embodiment, primary data source 102 is used for the storage ofprimary performance assessment data. For example, National FootballLeague (NFL) statistics, Major League Baseball (MLB) statistics, stockmarket data, personal fitness goals, and other performance data may bestored within data source 102. The data may be historical or live(real-time) data. In an alternative embodiment of the presentdisclosure, an optional secondary data source 103 is communicativelycoupled to processing application 101. Accordingly, the performance dataprovided to application 101 is not limited to primary performance dataand may optionally include user-generated (or secondary) data, which isgenerally defined as performance data that is based on the primaryperformance information. An example of secondary data includes theperformance of a fantasy football or baseball team. As fantasyparticipants claim players off of waivers, alter lineups, make draftdecisions, and watch their fantasy teams perform,user-generated/secondary data is produced to assess the performance ofthe fantasy team and players based on the primary performance datareflecting the actual player statistics. Another example user generatedperformance data includes stock market simulation games, where usersplay with pretend investment dollars.

As those of ordinary skill in the art would appreciate, primary datasource 102 as well as secondary data source 103 may be any type ofstorage device or storage medium such as hard disks, cloud storage,CD-ROMs, flash memory and DRAM. In other embodiments, it should beunderstood that processing application 101, primary data source 102, andsecondary data source 103 could reside on the same computing device oron different computing devices. Similarly, the performance data ofprimary data source 102 and/or secondary data source 103 could be storedwithin the processing application 101 or some other accessible server ordata storage device.

The system 100 further includes a processed database 104 coupled to theprocessing application 101 for storing processed data results of theapplication 101. Similar to data sources 102/103 as discussed above,database 104 may be any type of storage device or storage medium such ashard disks, cloud storage, CD-ROMs, flash memory and DRAM. The database104 can also reside on the same computing device as data sources102/103, processing application 101, or some other accessible server ordata storage device. Both processing application 101 and database 104are accessible over data network 105. Data network 105 can pertain to aglobal data network (e.g., the Internet), a regional data network, or alocal area network. Furthermore, the network 105 can also include one ormore wired or wireless networks.

User platform 106 accesses the services provided by processingapplication 101 as well as the processed performance data stored indatabase 104 via network 105. Platform 106 represents a variety of localor online applications where performance assessment is involved. Someexamples include, but are not limited to, professional sports, fantasysports, interactive gaming, physical fitness, social networking, stockmarkets, mutual funds, educational evaluation, and consumer sales.

A user, or plurality of users, 107 of user platform 106 accesses theperformance assessment data using client devices, for example 107A,107B, 107C and 107N, over data network 105. These client devices may becomputing devices such as laptops, desktops, cellular phones, personaldigital assistants (PDA), set top boxes, and so on. Communication withthe data network 105 can occur through a network data link, which can bea wired link and/or a wireless link. As is well known in the art, one ormore of the users 107 may also communicate with the processingapplication 101 or processed database 104 directly.

Data sources 102/103 are configured to store one or more elements of rawperformance data. However, as discussed above, as the number ofstatistical data stored increases, converting the data to useableknowledge can be time consuming and resource inefficient. Predictivemodels based on raw data must still be evaluated and, often, are noteasily understood.

One approach to address this issue is shown in FIG. 2, which illustratesa process 2000 that may be executed by system 100. Process 2000 beginswith raw performance data (starting block 2010), such as the datamaintained in data sources 102/103.

This raw data subsequently undergoes three major processing steps: (1)data mining (action block 2020); (2) category prioritization (actionblock 2030); and (3) nuggetization (action block 2040). As a result ofprocess 2000, automated notes are created from the raw data thatfacilitates performance assessment in a user-friendly approach.

In order to harness a wealth of raw performance information, process2000 uses data mining techniques to process and summarize raw data(action block 2020). With reference now to FIG. 3, start block 3010begins by collecting raw data associated with performance assessment andpopulating the Primary Data Source 102 (action block 3020). Currentsystems and methods exist for acquiring, collecting, exporting anddelivering performance assessment data. Furthermore, real-timeperformance data can be delivered, for example, using real-time locatingsystems (RTLS) and real-time sensing systems (RTSS) with RF technology.Local data can be acquired using sensors to measure physiologicalparameters as well as other empirical data. Additional information ondata acquisition/collection methods and techniques can be found, forexample, in U.S. Pat. No. 7,689,437 to Teller et al., filed Jun. 16,2000 for a “System for Monitoring Health, Wellness and Fitness,” andU.S. patent application Ser. No. 12/772,599, U.S. Publication No.2010/0283630, filed May 3, 2010, for “Sports Telemetry System forCollecting Performance Metrics and Data,” both of which are incorporatedby reference herewith in their entirety. Although other methods forcollecting data are available, existing databases compiled withperformance assessment data may also be used from known services, suchas, the Associated Press (AP), ESPN, Stats, Inc., Sports Data, GoogleFinance and Yahoo Finance, etc.

Once the raw data is available for processing, process 2000 subsequentlysummarizes the raw data into predefined categories for furtherprocessing (action block 3030). Using the sport of football as anexample, this raw data is summarized into two categories: (i) Statistics(e.g., pass attempts, completions, touchdowns, etc.) for players andteams; and (ii) Splits (e.g., by time-frame, opponent, side of field,time of game). Table 1 illustrates another example using baseball whereraw data from Data Source 102 may include information about a singleplayer's at-bat:

TABLE 1 GameID PlayerID BSCount PitchType PitchResult PrimaryEvent Etc.22232 99921 0-0 1 Missed 22232 99921 0-1 1 Ball 22232 99921 101 2Well-Hit GO

Each pitch of a single at-bat in Game 22232 for Player ID 99921 is shownin Table 1. Summarizing this raw data in action block 3030, Table 2illustrates an example of summarized data for three players over onegame whose raw data is similar to that shown in Table 1:

TABLE 2 Date PlayerID Hits At-Bats Walks RBI Etc. Jun. 11, 2011 99921 14 1 1 Jun. 11, 2011 99923 0 3 0 0 Jun. 11, 2011 99926 2 3 1 2

This summarized data is stored in processed database 104 (action block3040). Once the processed database is populated with summarized data,the data is then filtered and prioritized (action block 3050).Irrelevant or least relevant data is removed, and relevant data isflagged and assigned priority values (i.e., weight values) withindefined categories. Turning to FIG. 6, an illustration of a process 5000for filtering and prioritizing the data is shown. First, for eachcategory, the sample size is assessed to determine whether the samplesize is sufficiently large to provide meaningful analysis, e.g., if thesample size meets a pre-defined threshold (Action Block 5010). Forinstance, in the case of batting averages, players are grouped based onnumber of at-bat attempts in top, middle, and bottom tiers:

Bucket 1=subjects ranking in the 60_(th) percentile or higher attempts

Bucket 2=subjects ranking in the 16_(th)-59_(th) percentile in attempts

Bucket 3=subjects ranking in the bottom 15_(th) percentile in attempts

In this example, the players in “Bucket 3” are excluded from furtheranalysis due to insufficient sample size. The data within each bucket isthen ranked (Action Block 5020). An illustration of filtering andranking is shown below:

Player BAVG AB BAVG Rk Bucket Bucket Pcntl McCutchen, Andrew .362 417 11 100 Kemp, Matt .358 240 2 1 99.5 Smoak, Justin .189 344 232 1 0.4Buck, John .184 277 233 1 0 Ciriaco, Pedro .337 89 1 2 100 Dirks, Andy.333 168 2 2 99.4 Coghlan, Chris .140 93 172 2 0.5 Conrad, Brooks .13398 173 2 0 Perez, Hernan .500 2 1 3 100 Carrera, Ezequiel .414 29 2 399.4 Bianchi, Jeff .000 13 161 3 0 Vogt, Stephen .000 17 161 3 0

Another example of ranking data, using the examples provided in Tables 1and 2 above, is presented in Table 3:

TABLE 3 Ovrl Ovrl Hits— AB— BAVG— PlyerID StrtDate EndDate OvrlHitsAt-Bats Hits Rk fastball fastball FB Rk 99921 Jun. 1, 2011 Jun. 11, 20116 18 190 6 12 10 99923 Jun. 1, 2011 Jun. 11, 2011 11 35 32 8 19 36 99926Jun. 1, 2011 Jun. 11, 2011 2 21 281 1 15 265 99935 Jun. 1, 2011 Jun. 11,2011 0 1 450 0 1 450

In this example, the players are ranked according to both their overallbatting average (i.e., the ratio of total hits versus total at-bats) aswell as their fastball batting average (i.e., the ratio of hits versusfastballs at-bats). As Table 3 also illustrates, the data is not onlysummarized using statistics (e.g., at-bats and hits), but also usingsplits (e.g., time-frame split reflecting only games between Jun. 6,2011 to Jun. 11, 2011). A plurality of ranked tables covering varioustimeframes and splits for the summarized data is created (not shown)similar to Table 3. This plurality of ranked data is then stored inProcessed Database 104 (action block 3060) for use with categoryprioritization (end block 3070). As will be demonstrated below, thisinformation will enable the system 100 to generate noteworthy data andtrends not provided by previous systems known in the art.

Turning back to FIG. 2, category prioritization (action block 2030)classifies performance data using application specific schemes.Generally, in one embodiment, the standard classification schemesinclude seven categories: (1) granularity; (2) sample size significance;(3) performance extremes; (4) positive/negative impact; (5)circumstantial significance; (6) performance or tendency; and (7)comparison between timeframes. This classification scheme can also varybased on user input to processing application 101.

Applying this standard scheme to baseball, for example, (1) granularityindicates the level of detail for a specific statistic. For instance,the more splits, the more granular. Further, the lower averagedenominator for a category, the more granular the category tends to be.Some categories may be granular by nature, e.g., miss percentage ofswings may be more granular than overall batting average. Further,overall batting average would not be as granular as fastball battingaverage in the example above. (2) Sample size significance determinesthe relevance of each category based on the sample size (“attempts”),such that 250 overall at-bats are more significant than 120 overallat-bats. For instance, in the “Bucket” example above, sample sizesignificance is illustrated below:

Bucket 1 Minimum Bucket 1 Max STAT (Category) Denominator DenominatorOverall Batting Average 240 488 Fastball Slugging Percentage 160 320Breaking Ball Miss Percentage 223 465 Changeup Batting Average 87 125

Sorting the list above by column 2 (the minimum denominator to qualifyas Bucket 1 as described previously) would effectively provide thesample size significance value. If the user chose to only view data withhigh sample size, then likely the categories Fastball SluggingPercentage and Changeup Batting Average in the table above would beexcluded from the search results.

(3) Performance extremes identifies the statistically best and worst foreach category (e.g., league leaders in batting average and the leagueworst in batting average have higher performance extreme values comparedto those in the league in or around the average (50th percentile)). (4)Positive/negative impact reveals the influence of data to the performer(e.g., a 0.500 batting average against fastballs is positive to theplayer whereas a 0.100 batting average against fastballs is negative).Two factors will determine this in baseball statistics: whether a highnumber is good and what percentile the player falls into for thatcategory. (5) Circumstantial significance reveals the influence of datain relation to circumstantial variables (e.g., notes and data regardingan upcoming opponent). This considers both the current state and thehistory state, such as opponent strength, weather, and whether a team ishome or away.

(6) Performance or tendency: this category makes a distinction betweenwhether the Stat (Category) is performance based or technique/strategybased. For example, batting average is performance measure, butCurveball Usage for a pitcher or Pass Attempt % of Plays for a footballteam are more tendency or technique based.

(7) Comparison between timeframes: with this category, data betweendifferent time frames may be compared. Under this category, theprioritization may be based, at least in part, on the differencesbetween those time frames. For example, for Albert Pujols, if 2011 and2012 are selected as compared ranges, under other prioritizationschemes, priority of data may be based on comparison against leaguenormal data. However, under this scheme, higher priority may be given tostats/data where Pujols 2012 is significantly different from Pujols2011.

Category prioritization determines which information from the database104 is relevant to a certain category and is then assigned a priorityvalue within that category. For instance, from Table 3 above, Player99921 is ranked 10th in the league in batting average versus fastballssince Jun. 1, 2011. This relevant data is flagged and assigned a notestrength value for this category as shown in the table below:

Player 9921 (BAVG—“Batting Average”)

Beginning Size Note Size Percentile Strength Split Bucket StrengthStat-Split Category Timeframe Bucket Rank Value Adjust. Adjust. ValueBAVG High Pitches 1 1 10 80 x 1 x 1 80 BAVG Fastball 1 1 90 80 x 1 x 180 SLG High Pitches 1 1 12 76 x 0.1 x 1 7.6 BAVG Pitcher Ahead 3 1 30 40x 1 x 1 40 BAVG LHP 1 2 44 12 x 1 x 0.5 6 Miss % Fastball 4 1 52 4 x 1 x1 4 GB % Overall 1 1 88 76 x 1 x 1 76 Etc. . . .

Note Strength Value is determined by a formula that considers thefollowing three factors: 1) Percentile ranking for that stat category;2) Sample size Bucket; and 3) the type of Split.

Beginning Strength Value=ABS(50−Percentile Ranking)*2. The highestpossible strength value=100.

Split adjustment becomes 0.1 (or other value) multiplier through codethat knows whether a stat category with the same split has already beengiven a higher Note Strength Value. For example, in the above table,this hitter ranks poorly (10th and 12th percentile, respectively) in twovery similar Stat-Split categories: BAVG and SLG vs. “High Pitches”. Theprogram would apply a Split Adjustment to the SLG vs High Pitches toreduce its Note Strength Value because BAVG vs High Pitches already hasa high Note Strength attached to it (80).

Size Bucket Adjustment can be used to reduce the Note Strength for statcategories where the subject falls into Size Bucket 2 or 3, meaning theydo not have a significant sample size as those who were in Bucket 1 did.

Thus, in summarizing the table above, the two stat-split combinationswith the highest Note Strength Values are: Batting Average against HighPitches and Groundball Percentage Overall. SLG vs. high pitches is asignificant weakness for this player, but it's note value drops becausehis BAVG on high pitches is already higher in note priority. Categoriessuch as BAVG LHP and Miss % of Fastball have low Note Strengths becausethe player was near the league average (near 50th percentile) in thoseareas.

Once the remaining flagged items are assigned categorical priorities,process 2000 then converts (a.k.a., nuggetizes) the remainingfiltered/prioritized data to text strings (action block 2040). Withreference to FIG. 4, the data remaining in processed database 104 isobtained, each flagged data entry having a categorical priority value(start block 4010 and action block 4020). This data is converted tophrases (action block 4030) and these phrases are then processed intosentences (action block 4040), which can also be referred to asAutoNotes. These automated sentences are much more readable than thesummarized data. The sentences are based on predefined strings. Forexample, for the metric, fastball first pitch taken percentage, thestring would read as follows:

-   -   “Has taken your first pitch FB <Notable Zone><Numerator Notable        Zone> of <Denominator Notable Zone> times”. Thus, for notable        data in this category, the output would be generated as follows:        “Has taken your first pitch FB Down/Away 31 of 31 times.”

Below are some additional examples using Joe Mauer v. Right HandedPitchers:

-   -   Strength—Fastball (0.374) especially on first pitch (0.538).        Changeup (0.288) especially when he's ahead (0.333).    -   Best 2-strike pitch is CH/OT Middle/Down (0 well-hit out of 10        strikes)    -   Has taken first-pitch FB Down/Away 31 of 34 times    -   Has not chased FB when thrown Middle/In (only 3 chased of 25        out-of-zone pitches)    -   Weakness—CH/OT has been effective when Middle/Middle (0 well-hit        of 8 strikes)

Other examples using the data above produces sentences for player 99921and player 99926:

-   -   “Nationals outfielder Joe Smith is 6-for-18 (0.333 batting        average) since June 1.”    -   “Joe Smith of the Nationals is batting 0.500 (6-for-12) against        fastballs since June 1.”

“Bob Jones of the White Sox has only 2 hits in his last 21 at-bats.” Asaggregate data (e.g., league average, national average, etc.) is alsoavailable, additional information may also supplement the sentencecreated in action block 4040 (action block 4050). For example, appendingrelated batting average assessment for the league produces: “Joe Smithof the Nationals is batting 0.500 (6-for-12) against fastballs sinceJune 1; the league average against fastballs is 0.282.”

Each of these text strings and their associated priority values for eachcategory are then stored in a separate database (not shown) (actionblock 4060). Alternatively, the text strings and their priority valuesmay also be generated as needed. An example of stored text strings isillustrated in Table 5:

TABLE 5 Sample Perf. Special +/− to Subject Note Granularity SizeExtreme Signif. subject Joe Joe Smith is 1 7 7 0 9 Smith 6-for-18 (0.333batting average) since June 1. Joe Joe Smith is 7 8 9 0 10 Smith batting0.500 (6-for-12) against fastballs since June 1. Joe Joe Smith is 6 9 1010 1 Smith batting just 0.100 (2-for- 20) against tonight's opposingstarter.

The presentation of such data can be configured in a variety of ways.For instance, an AutoNote can be generated for an individual as part ofa specific group. For example, an AutoNote can be generated for hisperformance on his particular team: “Joe Smith leads all Twin hitterswith 20 HRs.”

The AutoNote may also reflect personal and/or team improvements. Forexample: Student X is averaging 90% on his geometry tests since December1, when he was averaging 69% on his previous tests.” Or “the Angels arebatting 0.320 against breaking pitches in 2012 whereas they were batting0.200 against breaking pitches in 2011.”

Trends and tendencies may also be presented in AutoNotes. For example,in the education setting, the system 100 can generate the followingAutoNote: “Student X earned a score of 90% or better in 8 straight testgrades.” In football, an AutoNote may generate: “Running Back Frank Gorehas run over 100 yards in 7 straight games.” A trend may also bepresented in the negative based on the noteworthy data above: “DerekJeter has not hit a fastball in the last 10 games.” Further, the datesof these trends and tendencies may be configured. For example, a usermay select 2011 and 2012 such that the AutoNote generates: “Derek Jeterbatted 0.160 versus right-handed breaking pitches in 2011 and batted0.270 against right handed pitches in 2012.” Moreover, the datagenerated from these two different timeframes may be compared and thedata may be presented based at least in part on the “Comparison BetweenTimeframes” category prioritization scheme described above.

This data can then be used with a variety of user platforms 106 (endblock 4070). For example, AutoNotes can be utilized with Twitter,Facebook messaging, or other social media and messaging platforms.AutoNotes enables the system 100 to discover and present note-worthypieces of information about a performer, such as a player, a student, acompany, or team, using user friendly language.

Referring to FIGS. 5a-b , additional sample user interfaces demonstratethe use of processing application 101. As illustrated, user platformsmay be interactive such that users may search and research rather thanviewing summaries and recaps. Searching for automated notes in a singleplayer is illustrated in FIG. 5a , while searching for automated notesreflecting an entire team/league is illustrated in FIG. 5 b.

Turning to FIGS. 7a-7g , exemplary applications of AutoNotes are shown.AutoNotes may be particularly suitable for popular fantasy sportsleagues, where stats are crucial to users of the leagues. AutoNotes canbe used to critique user actions in the form of Auto Smacks, shown inFIG. 7a or High-5s in FIG. 7b or Triumphs and Failures in FIG. 7e , oras general notes, as shown in FIGS. 7c or as trade suggestions in FIG.7d . AutoNotes can also be used for Report Cards, as shown in FIG. 7f orteam ratings, as shown in FIG. 7 g.

Turning to FIG. 8, a “drill down” feature may be added to an AutoNoteapplication, which enables a user to drill down on a particular AutoNotefor additional information. For example, for the AutoNote, “Tom Bradyhas completed 80% of his passes in the last 3 games,” a button or clickevent can trigger either more notes related to that, or a table ofinformation. For example: In the last 3 games: 90% completions torunning backs; 71% to Tight Ends; 64% to Wide Receivers; 88% to leftside; 77% to middle of field; 59% to right side, etc. . . . .

Although the previous embodiments were discussed primarily usingathletic performance assessment data, those skilled in the art wouldappreciate that alternative platforms 106 may benefit from processingapplication 101. This may be shown in the following examples:

Example 1

Using weight loss/physical fitness, category prioritization (actionblock 2030) may apply the standard classification scheme to physicalfitness assessment data. Granularity places priority on notes accordinglevel of detail: high granularity (e.g., routine workouts to generate“Susan has burned an average of 200 calories while bike riding onSundays in the past 10 weeks.”); medium granularity (e.g., time-framesplit diet to generate “Susan had 8 servings of vegetables since lastTuesday.”); and low granularity (e.g., single performances to generate“Susan ran 2 miles today.”).

Sample size significance classifies notes as follows: highly significantsample size (e.g., with a sample size of one year, “Susan has lost 30pounds (25 percent of her starting weight) in the last 365 days.”);medium sample size significance (e.g., with a sample size of a month,“Susan's weight has decreased from 140 pounds to 135 pounds thismonth.”); and low sample size significance (e.g., single day to generate“Susan did not exercise today.”).

Performance extremes classifies notes as follows: highly extreme (e.g.,superlative ranking, “Susan ranks 1st among her friends in percentagepounds lost in October (2% loss).”); medium extreme (e.g., “Susan'sranks 7th among her 15 friends in percentage weight lost since September(0.8% loss).”); and low extreme (e.g., average performance, “Susan ranksin the 50th percentile among users of this application with an averageof 1 serving of fruits per day this week.”).

Using a time-based circumstance, an example of highly circumstantialsignificance to a single day: “Susan's toughest day for exercise isWednesday. Try to get out there today (Wednesday) Susan!” Classifyingnotes having a positive/negative impact includes positive impact tofitness (e.g., “Susan lost 30 pounds (25 percent of her starting weight)in the last calendar year. Way to go Susan!”); and negative impact(e.g., “Susan has not exercised in the past 3 days.”).

Example 2

Using interactive gaming (e.g., online video poker), categoryprioritization (action block 2030) may apply the standard classificationscheme to gaming assessment data. Granularity places priority on notesaccording level of detail: high granularity (e.g., specific event togenerate “You have averaged +20 credits when being dealt a pair ofsevens or lower this month. The average player is even.” or “User hashit the green 18 of 20 times with his drive on the par-three 18th holeat Sawgrass in Tiger Woods Golf.”); medium granularity (e.g., lessdetailed event to generate “You have been dealt a pair 30 percent of thetime this week. The average player is dealt a pair 20 percent of thetime.” or “You have won the last 4 times you played John Smith and usedRoy Halladay as your starting pitched in MLB The Show.”); and lowgranularity (e.g., general detail to generate “You averaged −25 creditsthis week.” or “You have won the last 4 times you played John Smith inModern Warfare II.”).

Sample size significance classifies notes as follows: highly significantsample size (e.g., multiple attempts, “You won 295 hands and lost 304hands since June 1. That 49.2% winning hand percentage ranks in the 10thpercentile among players of this game.” or “Your overall record is 43wins and 20 losses in Madden Football over the past calendar year.”);medium sample size significance (e.g., mid-size sample, “You won 10 of20 times today when being dealt an Ace with no pair.” or “Your defenseis allowing just 2.2 yards per carry in Madden Football in the past 15games.”); and low sample size significance (e.g., a few events “You haveonly 1 win in the last 10 hands.” or “Your team has averaged 360 yardspassing in the last 3 games of Madden Football.”).

Performance extremes classifies notes as follows: highly extreme (e.g.,superlative ranking, “Between June 1 and June 15, your total winningsare +3000.” or “Your record is 0 wins and 20 losses in Grand TheftAuto.”); medium extreme (e.g., “You received a 3-of-a-kind 12 times in65 hands (18%) when being dealt a pair. The average player gets3-of-a-kind 14% of the time.” or “Your record is 12 wins and 8 losses inGrand Theft Auto.”); and low extreme (e.g., average performance, “Yourrecord is 10 wins and 10 losses in Grand Theft Auto.”).

Classifying notes having a positive/negative impact includes: positiveimpact to game strategy (e.g., “You were dealt a Big Hand (straight orbetter) 10 times in the last 125 deals for a gain of 1250.” or “Yourpitchers are averaging 11 strikeouts per game (normal average is 6 pergame) since Jun. 1, 2011 in MLB the Show.”); and negative impact (e.g.,“You have a current streak of 7 straight days with negative earnings,totaling −950.” or “Your pitchers are averaging 3 strikeouts per game(normal average is 6 per game) since Jun. 1, 2011 in MLB the Show.”).

Example 3

Using finance, category prioritization (action block 2030) may apply thestandard classification scheme to stock market assessment data.Granularity places priority on notes according level of detail: highgranularity (e.g., specific stock event “The most volatile stock in theS&P 500 in the past 60 days has been Netflix (NFLX), with a high of 242and a low of 129 in that timeframe.”); medium granularity (e.g., smallertime split “Citigroup (C) stock has risen 2.9% in the last 30 days; therest of the banking sector is down −12.3%.”); and low granularity (e.g.,general stock trend “Shares of Verizon (VZ) are down 9 percent since May1.”).

Sample size significance classifies notes as follows: highly significantsample size (e.g., “Southwest Airlines (LUV) stock has been positive 210days and negative 103 days in the last calendar year”); medium samplesize significance (e.g., “The biggest large-cap gainer in tech stocks inthe past 120 days has been Cypress Semiconductor (CY) with a 25.6%increase.”); and low sample size significance (e.g., “Shares of Bank ofAmerica (BAC) have risen 10 percent in the last three days.”).

Performance extremes classifies notes as follows: highly extreme (e.g.,superlative ranking, “If you purchased 100 shares of Apple (AAPL) onJan. 1, 2011, you have made $1,000 profit (+69%) as of today.”); mediumextreme (e.g., “If you purchased 100 shares of PepsiCo (PEP) on Jan. 1,2011, you have made $20 profit as of today (+0.02%).”); and low extreme(e.g., average performance, “If you purchased 100 shares of Nokia (NOK)on Mar. 1, 2011, you've lost $1,605 (−105%) as of today.”).

Using a time-based circumstance, an example of highly circumstantialsignificance to a single day: “Following a 4% or more price decreasesuch as yesterday's, General Electric (GE) tends to rise 3.2 percent thefollowing day (9 such occurrences).” Classifying notes having apositive/negative impact includes positive impact to investment strategy(e.g., “Wal-Mart (WMT) has gained 20.2 percent in the past six weeks.”);and negative impact (e.g., “Home Depot (HD) is down 18 percent in thepast six weeks.”).

In the foregoing specification, the invention has been described withreference to specific embodiments thereof. It will, however, be evidentthat various modifications and changes may be made thereto withoutdeparting from the broader spirit and scope of the invention. Forexample, the reader is to understand that the specific ordering andcombination of process actions described herein is merely illustrative,and the invention may appropriately be performed using different oradditional process actions, or a different combination or ordering ofprocess actions. For example, this invention is particularly suited forathletic-based performance assessment data, such as fantasy sports;however, the invention can be used for any performance assessment data.Additionally, and obviously, features may be added or subtracted asdesired. Accordingly, the invention is not to be restricted except inlight of the attached claims and their equivalents.

What is claimed is:
 1. An automated notes and categorization systemcomprising: a primary database, the primary database having rawperformance assessment data; wherein the primary database is operativelycoupled to a computer program product having a computer-usable mediumhaving a sequence of instructions which, when executed by a processor,causes said processor to execute an electronic process that analyzes andconverts said raw performance data; a processed database for storing theprocessed data operatively couple to the computer program product; andsaid electronic process comprising: data mining said performanceassessment data to obtain summarized data; prioritizing said summarizeddata based on user-defined weight values for a plurality ofclassification categories; and converting results of the prioritizationinto automated plain language notes.
 2. The system of claim 1, furthercomprising a secondary database having user-generated (secondary)performance assessment data.
 3. The system of claim 1, wherein saidprocess further comprises determining whether said summarized data ispositive or negative to the classification category.
 4. The system ofclaim 1, wherein the automated plain language notes include historicaltrend of said summarized data.
 5. The system of claim 4, wherein thehistorical trend is user-defined by beginning and end time periods. 6.The system of claim 1, wherein the process is further configured to postthe automated plain language notes to one or more third party socialmedia networks.